Every project matched to the right expert.
AI agents that read each project brief and every consultant profile, judge fit by meaning rather than keywords, and hand staffing teams an explained, ranked shortlist. The team still makes the final call.
- ~25% more efficient staffing: measured across the staffing operation.
- Faster from intake to shortlist: agents do the first pass through the whole talent pool.
- Explainable by design: every recommendation shows its reasons.
Meet our client
- Client
- Consulting & professional services platform
- Industry
- Professional services, consulting, talent platforms
- Market
- Global
In a nutshell
The client connects organisations with specialist consultants across industries and geographies. As the talent pool grew, staffing teams were still matching people to projects by hand, so shortlists were slow, outcomes depended on who did the matching, and good placements were missed. We built AI agents that parse project requirements, interpret consultant profiles, score fit with structured logic and produce ranked, explained shortlists, as decision support for staffing teams who keep the final say.
Keywords miss the best people
Staffing teams searched and screened by hand, and a keyword search only finds the words you type. The project treated matching as a question of meaning instead. Run the same brief both ways.
- Judgment-dependent: matching relied heavily on each person's knowledge of the pool.
- Slow screening: comparing consultants with requirements took significant time.
- Did not scale: the bigger the talent pool, the slower staffing became.
- Inconsistent: different teams produced different shortlists for similar roles.
- Missed placements: some opportunities were lost while shortlists were built.
- Under-used talent: available consultants went unnoticed, lowering utilisation.
A messy brief becomes a clear requirement
Project descriptions arrive as free text. A role-parsing agent normalises each one into a structured representation the system can compare. Pick a brief, parse it, then click any field to see where it came from.
Every CV, made comparable
Consultants describe the same skill a dozen ways. A profile agent reads experience, skills and availability and converts them into one comparable profile. Watch the raw phrases resolve.
A ranked shortlist, with the reasons attached
Matching logic scores each consultant on skills, experience, industry and availability together, then ranks them. Change what matters for this project and watch the order move. Open any consultant to see why.
AI recommends. Staffing teams decide.
The agents are a decision-support layer. Staffing teams review every recommendation and finalise placements, and because everyone starts from the same explained ranking, outcomes stop depending on who picked up the brief.
Agents inside the staffing workflow
Agent-based workflows connect to the client's staffing systems and data: project intake in, parsed requirements and interpreted profiles in the middle, an explained shortlist back to the team. Tap a component, or trace a project.
Trust matters as much as speed
Unstructured text on both sides, skills, experience and availability to weigh together, and a hard requirement that recommendations stay transparent and staffing teams stay in control. Flip each card.
A manual process became a scalable decision system
Following deployment, the matching system improved staffing efficiency and placement speed, and increased utilisation across a global talent pool.
The right expert. Every time.
AI agents that turn every brief into an explained shortlist, while your team keeps the final call.
- 01Your business objective
- 02The technical context
- 03The right next step